Balancing load across multiple computing resources can be an effective method of providing increased capacity, fault tolerance, and higher operating margins. Some computing tasks may be less time sensitive, and may therefore be accomplished using slower or less performant resources, which may also have the advantage of being more cost effective than higher performing resources.
Furthermore, some computing environments, such as cloud computing environments, provide for dynamic allocation of additional resources as existing resources become fully utilized. Dynamic allocation of additional resources may also incur additional expense, so these measures should not be taken unless truly needed to accomplish computing tasks effectively.
Measuring which resources are effectively utilized and which have excess capacity may be challenging in some environments. For example, spikes in utilization of a first computing resource may cause some implementations to inappropriately select other computing resources for particular computing tasks, such as reading and/or writing to a stable (e.g. non-transient) storage device, processing a network communication, or computing a result of a calculation, when those tasks may be more effectively performed by the first computing resource, despite its current utilization. Inappropriate resource selection can be costly in terms of reduced performance, increased cost, and decreased customer satisfaction. Therefore, improved methods of dynamically allocating loads or tasks to computing resources are needed.